relation: https://khub.utp.edu.my/scholars/2782/
title: Emotion detection using relative amplitude-based features through speech
creator: Mohan Kudiri, K.
creator: Md Said, A.
creator: Nayan, M.Y.
description: Automatic speech recognition analysis has been an active part in computer science for more than two decades. In general, to detect an emotion, long continuous signal is needed. Relative amplitude reduces bias of glottal mutation of speech wave amplitude and obtains a normalized measure without concern of information from being distinct in feature. Nonverbal communication plays crucial role in human-human or human-machine interpersonal relationships. In this paper, we propose the use of relative bin frequency coefficients for speech signal segmentation. Here, the support vector machine classifier is used to implement automatic emotion detection system. © 2012 IEEE.
date: 2012
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Mohan Kudiri, K. and Md Said, A. and Nayan, M.Y.  (2012) Emotion detection using relative amplitude-based features through speech.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867918939&doi=10.1109%2fICCISci.2012.6297301&partnerID=40&md5=5d16ad70ef5e2602b8509e95ea8abde1
relation: 10.1109/ICCISci.2012.6297301
identifier: 10.1109/ICCISci.2012.6297301